Stochastic discrete optimization
SIAM Journal on Control and Optimization
A method for discrete stochastic optimization
Management Science
Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems
Journal of Optimization Theory and Applications
Rates of convergence of ordinal comparison for dependent discrete event dynamic systems
Journal of Optimization Theory and Applications
Optimization via adaptive sampling and regenerative simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Stochastic Comparison Algorithm for Discrete Optimization with Estimation
SIAM Journal on Optimization
On Optimal Allocation of Indivisibles Under Uncertainty
Operations Research
Nested Partitions Method for Global Optimization
Operations Research
Intelligent Partitioning for Feature Selection
INFORMS Journal on Computing
Hybrid nested partitions algorithm for scheduling in job shop problem
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
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We analyze a new approach for simulation-based optimization of discrete event systems that draws on two recent stochastic optimization methods: an adaptive sampling approach called the nested partitions method and ordinal optimization. The ordinal optimization perspectives provides new insights into the convergence of the nested partitions method and guidelines for its implementation. We also use this approach to show that global convergence requires relatively simulation runs and propose new effective variants of the algorithm. Simulation results are presented to demonstrate the key results.